Publications (12)
Clustering-Guided Spatial-Spectral Mamba for Hyperspectral Image Classification
Zack Dewis, Yimin Zhu, Zhengsen Xu +4
Although Mamba models greatly improve Hyperspectral Image (HSI) classification, they have critical challenges in terms defining efficient and adaptive token sequences for improve p…
Bayesian Transformer for Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data
Mabel Heffring, Lincoln Linlin Xu
Although high-resolution mapping of Pan-Arctic sea ice with reliable corresponding uncertainty is essential for operational sea ice concentration (SIC) charting, it is a difficult…
A Novel Graph-Regulated Disentangling Mamba Model with Sparse Tokens for Enhanced Tree Species Classification from MODIS Time Series
Motasem Alkayid, Zhengsen Xu, Saeid Taleghanidoozdoozan +7
Although tree species classification from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data is critical for supporting various environmental applications, it i…
Knowledge-Aware Mamba for Joint Change Detection and Classification from MODIS Times Series
Zhengsen Xu, Yimin Zhu, Zack Dewis +4
Although change detection using MODIS time series is critical for environmental monitoring, it is a highly challenging task due to key MODIS difficulties, e.g., mixed pixels, spati…
White-Box mHC: Electromagnetic Spectrum-Aware and Interpretable Stream Interactions for Hyperspectral Image Classification
Yimin Zhu, Lincoln Linlin Xu, Zhengsen Xu +6
In hyperspectral image classification (HSIC), most deep learning models rely on opaque spectral-spatial feature mixing, limiting their interpretability and hindering understanding…
Sparse Deformable Mamba for Hyperspectral Image Classification
Lincoln Linlin Xu, Yimin Zhu, Zack Dewis +4
Although Mamba models significantly improve hyperspectral image (HSI) classification, one critical challenge is the difficulty in building the sequence of Mamba tokens efficiently.…